We Still Don’t Know How to Taper Glucocorticoids in Rheumatoid Arthritis, and We Can Do Better
Bibliographic record
Abstract
Rheumatologists, internists, residents, and fellows frequently ask, “How do you taper glucocorticoids in rheumatoid arthritis?” Despite controlling symptoms of rheumatoid arthritis (RA)1 and slowing progression of radiological joint damage in early RA2, low-dose glucocorticoids (GC) are associated with a plethora of chronic adverse effects, including diabetes mellitus, hypertension, atherosclerosis, weight gain, osteoporosis, skin fragility, Cushingoid appearance, and myopathy, and are also associated with increased risk of infection, cardiovascular events, depression, cataracts, and skin atrophy3,4. To prevent or minimize these effects, GC tapering to the lowest dose necessary to maintain control of disease activity is recommended5. However, strategies to taper GC vary, and are mostly described based on expert opinion. No clinical trials have directly examined, much less compared, different GC tapering regimens in RA5. Withdrawal of GC may also precipitate adverse events, including flare, adrenal insufficiency, and GC withdrawal syndrome6. To attempt to answer this frequent question, we performed a systematic literature review to assess the influence of tapering regimens on successful GC withdrawal, as well as clinical outcomes. We searched PubMed and Cochrane Central to identify publications from January 1972 to February 2011 (detailed search strategy available on request). Search terms comprised 4 blocks that were combined with Cochrane hedge, “methodological filter for clinical trials.” The first block addressed disease (RA in adults); the second and third, intervention (GC/related terms AND tapering); and the fourth, outcome (withdrawal/dose reduction, effect on disease activity). We double-extracted all titles and abstracts according to the following: inclusion criteria: (a) adult patients with RA; (b) studies in which GC and related terms (e.g., corticosteroids, prednisone, prednisolone, etc.) were used; and exclusion criteria: (a) case report or case series with < 20 patients; (b) editorials, review articles, letters, opinions, etc. Two of … Address correspondence to Dr. D.E. Furst, Division of Rheumatology, Department of Medicine, University of California, Los Angeles, 32-59 Rehabilitation Center, 1000 Veteran Avenue, Los Angeles, CA 90095, USA. E-mail: defurst{at}mednet.ucla.edu
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".